Ke Han

Southwest Jiaotong University · School of Transportation and Logistics

Drive-by sensing (i.e. vehicle-based mobile sensing) is an emerging data collection paradigm that leverages vehicle mobilities to scan a city at low costs. It represents a positive social externality of urban transport activities. Bus transit systems are widely considered in drive-by sensing due to extensive spatial coverage, reliable operations, a...

Vehicle-based mobile sensing, also known as drive-by sensing, efficiently surveys urban environments at low costs by leveraging the mobility of urban vehicles. While recent studies have focused on drive-by sensing for fleets of a single type, our work explores the sensing power and cost-effectiveness of a mixed fleet that consists of vehicles with...

This study explores the potential of using ride-hailing vehicles (RVs) for integrated passenger and freight transport based on shared mobility. In this crowd-sourced mode, ride-hailing platforms can profit from parcel delivery services, and logistics companies can reduce operational costs by utilizing the capacities of RVs. The Share-a-Ride problem...

Earthwork-related locations (ERLs), such as construction sites, earth dumping ground, and concrete mixing stations, are major sources of urban dust pollution (particulate matters). The effective management of ERLs is crucial and requires timely and efficient tracking of these locations throughout the city. This work aims to identify and classify ur...

Identifying and managing high-emitters among heavy-duty diesel vehicles is a key to mitigating urban air pollution, as a small number of such vehicles could contribute a significant amount of total transport emissions. On-board monitoring (OBM) systems can directly monitor the real-time emission performance of heavy-duty vehicles on road and have b...

Transport policies targeting emission reduction and air quality improvement typically discourage private car use and promote shifts to mass transit (bus, metro). However, possible increase of travelers' exposure while adapting to those policies have often been overlooked, which is addressed in this work via a case study of Heavy Pollution Episodes...

Vehicle-based mobile sensing is an emerging data collection paradigm that leverages vehicle mobilities to scan a city at low costs. Certain urban sensing scenarios require dedicated vehicles for highly targeted monitoring, such as volatile organic compounds (VOCs, a type of air pollutant) sensing, road surface monitoring, and accident site investig...

Fugitive road dust (FRD), as one of the major pollutants in the city, poses great harm to the environment and the physical health of citizens. A common countermeasure adopted by government agencies is employing on-road water trucks (sprinklers) to spray water (sprinkle) on urban streets to reduce the FRD. Currently, the traveling routes of sprinkle...

Construction waste hauling trucks (or 'slag trucks') are among the most commonly seen heavy-duty diesel vehicles in urban streets, which not only produce significant carbon, NOx and PM2.5 emissions but are also a major source of on-road and on-site fugitive dust. Slag trucks are subject to a series of spatial and temporal access restrictions by loc...

Previous studies on exposure have often omitted the impact of travelers' response to policies during air pollution, particularly for multi-modal traffic system. To address this gap, we investigate the population exposure of three modes (cars, buses, and metro) during morning rush hour in Chengdu, China, amidst the implementation of contingent traff...

我国重型柴油货车的尾气排放在各类车型中占比达70.4%（氮氧化物）和51.9%（颗粒物），是城市交通污染的重要来源。建筑垃圾运输车（或“运渣车”）是我国大中型城市最常见的一类重型柴油货车，且易产生扬尘污染，被广泛纳入全国各地生态环境部门大气污染防治行动方案。本文围绕运渣车活动全周期监管，系统性提出基于数智化技术的一体化监管体系和新能源化减排路径，并结合在成都市的落地实践进行探讨。具体而言：（1）融合GPS轨迹、车载诊断系统（OBD）、尾气遥感监测等多源数据，基于人工智能算法形成“车辆-运企-点位”一体化的数智化管控体系；（2）开展该数智化体系在运渣车管控政策下的效果评估，包括车辆时空分布、里程和排放的演化；（3）探索基于“政策-管控-市场”三大要素闭环的运渣车新能源化路径。本文以环保为切入点...

Construction waste hauling trucks (CWHTs), as one of the most commonly seen heavy-duty vehicles in major cities around the globe, are usually subject to a series of regulations and spatial-temporal access restrictions because they not only produce significant NOx and PM emissions but also causes on-road fugitive dust. The timely and accurate predic...

Construction waste hauling trucks (CWHTs), as one of the most commonly seen heavy-duty vehicles in major cities around the globe, are usually subject to a series of regulations and access restrictions because they not only produce significant NOx and PM emissions but also causes on-road fugitive dust. To limit their environmental impact, specific s...

This paper explores the concept of fairness and equitable matching in an on-line vehicle-cargo matching setting, addressing the varying degrees of satisfaction experienced by shippers and carriers. Relevant indicators for shippers and carriers in the on-line matching process are categorized as attributes, expectations, and reliability, which are su...

This paper presents a macroscopic fundamental diagram model with volume-delay relationship (MFD-VD) for road traffic networks, by exploring two new data sources: license plate cameras (LPCs) and road congestion indices (RCIs). We derive a first-order, non-linear and implicit ordinary differential equation involving the network accumulation (the vol...

Vehicular crowd-sensing (VCS) has become a widely adopted approach in smart city applications, with online ride-hailing system as a promising carrier. However, the spatially unbalanced demand of online ride-hailing services makes it challenging to collect urban information comprehensively. This paper explores the potential of vehicle-passenger matc...

Drive-by sensing (i.e. vehicle-based mobile sensing) is an emerging data collection paradigm that leverages vehicle mobilities to scan a city at low costs. It represents a positive social externality of urban transport activities. Bus transit systems are widely considered in drive-by sensing due to extensive spatial coverage, reliable operations, a...

Network traffic state prediction has been constantly challenged by complex spatiotemporal features of traffic information as well as imperfection in streaming data. This paper proposes a traffic flow prediction model for spatiotemporal graph networks based on fusion of attention mechanisms (STGNN-FAM) to simultaneously tackle these challenges. This...

This book presents advanced research in a relatively new field of scholarly inquiry that is usually referred to as dynamic network user equilibrium, now almost universally abbreviated as DUE. It provides the first synthesis of results obtained over the last decade from applying the differential variational inequality (DVI) formalism to study the DU...

In this section we present closed-form solutions of DUEs with fixed demand (Sect. 9.1.1) and elastic demand (Sect. 9.1.3) in a simple network depicted in Fig. 9.1.

In order to apply the results developed in Chap. 4 regarding the relationship of dynamic user equilibria to differential variational inequalities (DVI), we must be able to compute the solutions to differential variational inequalities. It should come as no surprise that there is often an equivalent functional fixed-point problem corresponding to a...

In this chapter, we present an infinite-dimensional variational inequality (VI) formulation of the continuous-time simultaneous route-and-departure-time (SRDT) dynamic user equilibrium.

This chapter presents an existence theory for a variety of dynamic user equilibria. Analytical DUE models tend to be of two varieties: (1) route choice (RC) user equilibrium (Friesz et al., Oper Res 37(6): 893–901, 1989; Merchant and Numhauser, Transp Sci 12(3):183–199, 1978; Merchant and Numhauser, Transp Sci 12(3): 200–207, 1978; Mounce, Transp R...

Central to the presentation and understanding of dynamic user equilibrium models is a sound mathematical foundation in a number of inter-related topics. This chapter aims to provide a quick overview of knowledge essential to the formulation, analysis, and computation of DUE models. These subjects include functional analysis, the calculus of variati...

Another important class of traffic flow models, arguably more realistic than non-physical queue models, captures the formation, propagation, and dissipation of physical queues and allows spillback to be explicitly modeled. The most widely used physical queue models are the Lighthill-Whitham-Richards model, as well as its discrete forms including th...

An essential component of many DTA models is a procedure known as dynamic network loading (DNL). The DNL subproblem aims at describing and predicting the spatial-temporal evolution of traffic flows on a network that is consistent with established route and departure time choices of travelers, by introducing appropriate dynamics to flow propagation,...

In this chapter, we present the differential variational inequality (DVI) formalism and use it to relate the much studied calculus of variations, including the theory of optimal control, to differential Nash games. The games we consider are motivated by the notion of dynamic user equilibrium as presented in the previous chapter.

Vehicle-based mobile sensing (a.k.a drive-by sensing) is an important means of surveying urban environment by leveraging the mobility of public or private transport vehicles. Buses, for their extensive spatial coverage and reliable operations, have received much attention in drive-by sensing. Existing studies have focused on the assignment of senso...

Pervasive and mobile sensing is an integral part of smart transport and smart city applications. Vehicle-based mobile sensing, or drive-by sensing (DS), is gaining popularity in both academic research and field practice. The DS paradigm has an inherent transport component, as the spatial-temporal distribution of the sensors are closely related to t...

This study analyzes the potential impact of Brexit on port congestion and supply chain cost using the example of the largest European Ro-Ro port of Dover. A microscopic simulation model is constructed to replicate traffic conditions and analyse different scenarios. The results show that the current port infrastructure will have difficulties facing...

Vehicle-based mobile sensing (a.k.a drive-by sensing) is an important means of surveying urban environment by leveraging the mobility of public or private transport vehicles. Buses, for their extensive spatial coverage and reliable operations, have received much attention in drive-by sensing. Existing studies have focused on the assignment of senso...

Airport surface operations, such as off-block control, taxi routing, and runway sequencing, are typically confined to a fixed network topology with given origin–destination (O-D) pairs for departures and arrivals. Reconfiguring the O-D distribution of flights by actively assigning their aprons and runways is a potentially effective measure to maxim...

The dispatch and routing problems of shared autonomous vehicle (DR-SAV) have been widely studied, and one of the key challenges is the modeling complexity and computational burden associated with network congestion. Existing studies on DR-SAV are limited in the sense that traffic congestion and demand–supply interaction, resulting from dispatch and...

1. Two modules for dynamic traffic assignment. 2. A solver for variational inequalities problem (translated from the Matlab codes of Prof. Bingsheng He (何炳生)). 3. A novel solver for surrogate-based modeling. The outputs of the RSM-DUE may differ slightly from the tables in the paper due to the random factors introduced in the solution algorithm....

In this paper we present sufficient conditions for convergence of projection and fixed-point algorithms used to compute dynamic user equilibrium with elastic travel demand (E-DUE). The assumption of strongly monotone increasing path delay operators is not needed. In its place, we assume path delay operators are merely weakly monotone increasing, a...

Runway systems are among the most stringent bottlenecks at global hub airports, which have been identified as a major source of airport inefficiency. Runway system inefficiencies are manifested in multiple dimensions such as delay, throughput reduction and excessive emission, whose tradeoffs are investigated in this paper as part of an airport runw...

This paper uses empirical traffic data collected from three locations in Europe and the US to reveal a three-phase fundamental diagram with two phases located in the uncongested regime. Model-based clustering, hypothesis testing and regression analyses are applied to the speed–flow–occupancy relationship represented in the three-dimensional space t...

Reactionary delays that propagate from a primary source throughout train journeys are an immediate concern for British railway systems. Complex non-linear interactions between various spatiotemporal variables govern the propagation of these delays which can avalanche throughout railway network causing further severe disruptions. This paper introduc...

This paper proposes a hierarchical Bayesian network model (BNM) to quantitatively evaluate the resilience of urban transportation systems. Based on systemic thinking and taking a sustainability perspective, we investigate the long-term resilience of the road transportation systems in four cities in China from 1998 to 2017, namely Beijing, Tianjin,...

Mobile and ubiquitous sensing of urban air quality has received increased attention as an economically and operationally viable means to survey atmospheric environment with high spatial-temporal resolution. This paper proposes a machine learning based mobile air pollution sensing framework, coined Deep-MAPS, and demonstrates its scientific and fina...

This paper proposes a hierarchical Bayesian network model (BNM) to quantitatively evaluate the resilience of urban transportation systems. Based on systemic thinking and taking a sustainability perspective, we investigate the long-term resilience of the road transportation systems in four cities in China from 1998 to 2017, namely Beijing, Tianjin,...

Understanding the impact of aircraft speed heterogeneity on air traffic operation is crucial for airspace design and air traffic flow management. Speed heterogeneity (or aircraft mix) is recognized as a causal factor for complexity in air traffic operations, through qualitative or statistical analysis. Quantitative metrics on how it affects current...

We propose a real-time signal control framework based on a nonlinear decision rule (NDR), which defines a nonlinear mapping between network states and signal control parameters to actual signal controls based on prevailing traffic conditions, and such a mapping is optimized via off-line simulation. The NDR is instantiated with two neural networks:...

Urban traffic congestion has an obvious spatial and temporal relationship and is relevant to real traffic conditions. Traffic speed is a significant parameter for reflecting congestion of road networks, which is feasible to predict. Traditional traffic forecasting methods have poor accuracy for complex urban road networks, and do not take into acco...

This paper presents a doubly dynamic day-today (DTD) traffic assignment model with simultaneous route-and-departure-time (SRDT) choices while incorporating incomplete and imperfect information as well as bounded rationality. Two SRDT choice models are proposed to incorporate imperfect travel information: One based on multinomial Logit (MNL) model a...

Large-scale evacuations constitute common life-saving exercises that are activated in many disaster response campaigns. Their effectiveness is often inhibited by traffic congestion, disrupted and imperfect coordination mechanisms, and the poor state of the underlying transportation networks. To address this problem, this paper presents a hybrid sim...

This paper proposes a new dynamic user equilibrium (DUE) traffic assignment model using reservoir-based network reduction techniques and surrogate dynamic network loading models. A traffic network is decomposed into a reservoir structure, and the DUE problem is formulated as a variational inequality, with an embedded surrogate model for the path de...

This paper proposes a framework for airport taxi situation awareness to enhance the assessment of aircraft ground movements in complex airport surfaces. Through a macroscopic distribution network (MDN) of arrival and departure taxi processes in a spatial-temporal domain, we establish two sets of taxi situation indices (TSIs) from the perspectives o...

Dynamic user equilibrium (DUE) is the most widely studied form of dynamic traffic assignment (DTA), in which road travelers engage in a non-cooperative Nash-like game with departure time and route choices. DUE models describe and predict the time-varying traffic flows on a network consistent with traffic flow theory and travel behavior. This paper...

Vehicle trajectory data collected via GPS-enabled devices have played increasingly important roles in estimating network-wide traffic, given their broad spatial-temporal coverage and representativeness of traffic dynamics. This paper exploits taxi GPS data, license plate recognition (LPR) data, and geographical information for reconstructing the sp...

This paper proposes a hierarchical Bayesian network model (BNM) to quantitatively evaluate the resilience of urban transportation infrastructure. Based on systemic thinkings and sustainability perspectives, we investigate the long-term resilience of the road transportation systems in four cities of China from 1998 to 2017, namely Beijing, Tianjin,...

We proposed a Bayesian network model (BNM) based on function-oriented resilience framework and ontological interdependence among 10 system qualities to probabilistically assess the general resilience of the road transportation system in Beijing from 1997 to 2016. We tested the model with multi-source data collected from various sectors. The system...

Mobile and ubiquitous sensing of urban air quality has received increased attention as an economically and operationally viable means to survey atmospheric environment with high spatial-temporal resolution. This paper proposes a machine learning based mobile air pollution sensing framework, called Deep-MAPS, and demonstrates its scientific and fina...

This paper focuses on the demand-oriented passenger train scheduling problem for a congested urban rail line, simultaneously considering uneven spatial and temporal demand distributions. To account for the left-behind phenomenon and passenger selection behavior, a passenger-train interaction framework is developed to dynamically assign passengers t...

We propose a real-time signal control framework based on a nonlinear decision rule (NDR), which defines a nonlinear mapping between network states and signal control parameters to actual signal controls based on prevailing traffic conditions, and such a mapping is optimized via off-line simulation. The NDR is instantiated with two neural networks:...

We proposed a Bayesian Network Model (BNM) based on function-oriented resilience framework and ontological interdependence among 10 system qualities to probabilistically assess the general resilience of the road transportation system in Beijing from 1997 to 2016. We tested the model with multi-source data collected from various sectors. The system...

This paper proposes a new dynamic user equilibrium (DUE) traffic assignment model using reservoir-based network reduction techniques and surrogate dynamic network loading models. A traffic network is decomposed into a reservoir structure, and the DUE problem is formulated as a variational inequality, with an embedded surrogate model for the path de...

This paper presents a doubly dynamic day-to-day (DTD) traffic assignment model with simultaneous route-and-departure-time (SRDT) choices while incorporating incomplete and imperfect information as well as bounded rationality. Two SRDT choice models are proposed to incorporate imperfect travel information: One based on multinomial Logit (MNL) model...

This paper presents a doubly dynamic day-to-day (DTD) traffic assignment model with simultaneous route-and-departure-time (SRDT) choices while incorporating incomplete and imperfect information as well as bounded rationality. Two SRDT choice models are proposed to incorporate imperfect travel information: One based on multinomial Logit (MNL) model...

This is a simple demonstration of the Matlab package that solves the dynamic network loading (DNL) problem on the Braess network. See more information at https://link.springer.com/article/10.1007/s11067-018-9433-y

This is a Matlab package for solving the dynamic user equilibrium problem on the Anaheim network (416 nodes, 914 links, 1,406 OD pairs, 30,719 paths). See documentation at https://link.springer.com/article/10.1007/s11067-018-9433-y

This is a Matlab package for solving the dynamic user equilibrium problem on the Sioux Falls network (24 nodes, 76 links, 530 OD pairs, 6180 paths). See documentation at https://link.springer.com/article/10.1007/s11067-018-9433-y

This is a Matlab package for solving the dynamic user equilibrium problem on the Braess network (4 nodes, 5 links, 4 OD pairs, 8 paths). See documentation at https://link.springer.com/article/10.1007/s11067-018-9433-y

This is a Matlab package for solving the dynamic user equilibrium problem on the Nguyen network (13 nodes, 19 links, 4 OD pairs, 24 paths). See documentation at https://link.springer.com/article/10.1007/s11067-018-9433-y

This is a Matlab package for solving the dynamic user equilibrium problem on the Chicago Sketch network (933 nodes, 2,950 links, 86,179 OD pairs, 250,000 paths). See documentation at https://link.springer.com/article/10.1007/s11067-018-9433-y

This gif file visualises the dynamic user equilibrium solution on the Chicago Sketch network. The colour scale indicates the relative link outflow (defined as the link's time-varying outflow over the link flow capacity). Note that the junction model employs the continuous signal control model, where the link outflow is treated as the time-average o...

We apply a novel clustering technique to London's bikesharing network, deriving distinctive behavioral patterns and assessing community interactions and spatio-temporal dynamics. The analyses reveal self-contained, interconnected and hybrid clusters that mimic London's physical structure. Exploring changes over time, we find geographically isolated...

Dynamic user equilibrium (DUE) is the most widely studied form of dynamic traffic assignment, in which road travelers engage in a non-cooperative Nash-like game with departure time and route choices. DUE models describe and predict the time-varying traffic flows on a network consistent with traffic flow theory and travel behavior. This paper docume...

Vehicle trajectory data collected via GPS-enabled devices have played increasingly important roles in estimating network-wide traffic, given their broad spatial-temporal coverage and representativeness of traffic dynamics. This paper exploits taxi GPS data, license plate recognition (LPR) data and geographical information for reconstructing the spa...

Dynamic user equilibrium (DUE) is the most widely studied form of dynamic traffic assignment, in which road travelers engage in a non-cooperative Nash-like game with departure time and route choices. DUE models describe and predict the time-varying traffic flows on a network consistent with traffic flow theory and travel behavior. This paper docume...

Vehicle trajectory data collected via GPS-enabled devices have played increasingly important roles in estimating network-wide traffic, given their broad spatial-temporal coverage and representativeness of traffic dynamics. This paper exploits taxi GPS data, license plate recognition (LPR) data and geographical information for reconstructing the spa...

This paper uses empirical traffic data collected from three locations in Europe and the US to reveal a three-phase fundamental diagram with two phases located in the uncongested regime. Model-based clustering, hypothesis testing, and regression analyses are applied to the speed-flow-occupancy relationship represented in the three-dimensional space...

This paper proposes a dual-timescale , day-today dynamic traffic assignment model that takes into account variable message signs (VMS) and its interactions with drivers' travel choices and adaptive learning processes. The within-day dynamic is captured by a dynamic network loading problem with en route update of path choices influenced by the VMS;...

This paper proposes a dual-time-scale, day-to-day dynamic traffic assignment model that takes into account variable message signs (VMS) and its interactions with drivers' travel choices and adaptive learning processes. The within-day dynamic is captured by a dynamic network loading problem with en route update of path choices influenced by the VMS;...

This paper is pedagogic in nature, meant to provide researchers a single reference for learning how to apply the emerging literature on differential variational inequalities to the study of dynamic traffic assignment problems that are Cournot-like noncooperative games. The paper is presented in a style that makes it accessible to the widest possibl...

Real-time traffic signal control with environmental objectives has been a difficult challenge due to (1) the highly dynamic and uncertain nature of road traffic and their emission profile; (2) the need for generating timely and robust decisions for large-scale networks; (3) the incorporation of multi-source and heterogeneous data; and (4) the balan...

The rapid growth in air traffic has resulted in increased emission and noise levels in terminal areas, which brings negative environmental impact to surrounding areas. This study aims to optimize terminal area operations by taking into account environmental constraints pertaining to emission and noise. A multi-objective terminal area resource alloc...

Invulnerable metro systems are essential for the safety and efficiency of urban transportation services. Therefore, it is of significant interest to systematically assess the vulnerability of metro systems. To this end, in this paper, we assess the vulnerability of metro systems with a data-driven framework in which dynamic travel patterns are cons...

Bikesharing schemes are transportation systems that not only provide an efficient mode of transportation in congested urban areas, but also improve last-mile connectivity with public transportation and local accessibility. Bikesharing schemes around the globe generate detailed trip data sets with spatial and temporal dimensions, which, with proper...

Bikesharing schemes are transportation systems that not only provide an efficient mode of transportation in congested urban areas, but also improve last-mile connectivity with public transportation and local accessibility. Bikesharing schemes around the globe generate detailed trip data sets with spatial and temporal dimensions, which, with proper...

This paper proposes a discrete field cellular automaton (CA) model that integrates pedestrian heterogeneity, anisotropy, and time-dependent characteristics. The pedestrian movement direction, moving/staying, and steering are governed by the transfer equations. Compared with existing studies on fine-discretized CA models, the proposed model is advan...

The fact that road transportation negatively affects the quality of the environment and deteriorates its bearing capacity has drawn a wide range of concerns among researchers. In order to provide more realistic traffic data for estimations of environmental impacts, dynamic traffic assignment (DTA) models have been adopted in transportation planning...